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feat: added links to readme
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agherasie committed Dec 24, 2023
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# ~ PoC AI Pool 2024 ~

- ## Day 2: Neural Networks from Scratch
- ### Module 1: Linear Regression
- **Notebook:** [`linear-regression.ipynb`](./linear-regression.ipynb)
- ### Module 2: Logistic Regression
- **Notebook:** [`logistic-regression.ipynb`](./logistic-regression.ipynb)
- ### Module 3: Deep Learning
- **Notebook:** [`deep-learning.ipynb`](./deep-learning.ipynb)
-----------
- ### Module 1: Linear Regression
- **Notebook:** [`linear-regression.ipynb`](./linear-regression.ipynb)
- ### Module 2: Logistic Regression
- **Notebook:** [`logistic-regression.ipynb`](./logistic-regression.ipynb)
- ### Module 3: Deep Learning
- **Notebook:** [`deep-learning.ipynb`](./deep-learning.ipynb)

---

**Hooray : You've made it to AI !**
On today's menu, we'll enter the wonderful world of machine learning with two major algorithms : Linear and Logistic Regression, followed by the modern approach of Deep Learning using PyTorch !

> Here's a list of resources that we believe can be useful to follow along (and that we've ourselves used to learn these topics before being able to write the subjects):
## Module 1

- [3b1b's neural network series](https://www.youtube.com/watch?v=aircAruvnKk&list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi)
- [sentdex's NNFS series & book](https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3)
- [andriy burkov's 100-page ML book](https://themlbook.com/) particularly [chapter 4](https://www.dropbox.com/s/xpd5x6p6jte3th5/Chapter4.pdf?dl=0)
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## Module 2

## Module 3
- [derivating bce loss](https://www.google.com/search?sca_esv=593424282&rlz=1C5CHFA_enFR1086FR1086&sxsrf=AM9HkKnhFXQw46XVx7yP5nyzZOxkebfGWw:1703424283700&q=sigmoid&tbm=isch&source=lnms&sa=X&sqi=2&ved=2ahUKEwiXptP6laiDAxVBU6QEHVYpCxIQ0pQJegQIDhAB&biw=1512&bih=738&dpr=2)

## Module 3

- [pytorch docs](https://pytorch.org/docs/stable/index.html)
- [install pytorch](https://pytorch.org/get-started/locally/)

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